To evaluate the quality of the learned representations from fundus images, we perform linear evaluation and transfer capacity evaluation on the partial datasets. A state-of-the-art CL method SimCLR [1]} is adopted as our baseline for comparison, which nevertheless takes an entire fundus image as the input for the contrastive prediction task. In Table REF , SimCLR (128 \(\times \)  128) denotes the multiple views of fundus images are resized to 128 \(\times \)  128 as the input for the contrastive prediction task in SimCLR, being consistent with the input size of our lesion-based CL framework. However, crop-and-resize transformation may critically change the pixel size of the input. SimCLR (224 \(\times \)  224) experiments are conducted based on the consideration that aligning the pixel size of the input for CL and that for downstream tasks may achieve better performance. The ResNet50 model in the fully-supervised DR grading is initialized with parameters from a model trained on the ImageNet [2]} dataset. Training curves are provided in Fig. A3 of the appendix.
